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Attention-deficit/hyperactivity disorder (ADHD) is the most common comorbidity in individuals with Tourette syndrome (TS). Yet, it is unclear to what extent TS and ADHD show overlapping or distinct neural abnormalities. ADHD has been associated with altered reward processing, but there are very few studies on reward processing in TS. This study assessed neural activation of basal ganglia and thalamus during reward anticipation and receipt in children with TS and/or ADHD. We analysed mean activations of a priori specified regions of interest during an fMRI monetary incentive delay task. Data was used from 124 children aged 8-12 years (TS nâ¯=â¯47, of which 29 had comorbid ADHD; ADHD nâ¯=â¯29; healthy controls nâ¯=â¯48). ADHD severity across ADHD and TS groups and healthy controls was marginally related to hypoactivation of the right nucleus accumbens during reward anticipation; this effect was not moderated by TS diagnosis. We detected no associations of neural activation with TS. The association between ADHD severity and hypoactivation of the right nucleus accumbens during reward anticipation, independent of the presence or absence of TS, is in line with the view of nucleus accumbens hypoactivation as a dimensional, neurofunctional marker of ADHD severity, transcending the boundaries of primary diagnosis.

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Objective: Aggressive behavior is among the most common reasons for referral to psychiatric clinics and confers significant burden on individuals. Aggression remains poorly defined; there is currently no consensus on the best ways to recognize, diagnose, and treat aggression in clinical settings. In this review, we synthesize the available literature on aggression in children and adolescents and propose the concept of impulsive aggression (IA) as an important construct associated with diverse and enduring psychopathology. Methods: Articles were identified and screened from online repositories, including PubMed, PsychInfo, the Cochrane Database, EMBase, and relevant book chapters, using combinations of search terms such as "aggression," "aggressive behavio(u)r," "maladaptive aggression," "juvenile," and "developmental trajectory." These were evaluated for quality of research before being incorporated into the article. The final report references 142 sources, published from 1987 to 2019. Results: Aggression can be either adaptive or maladaptive in nature, and the latter may require psychosocial and biomedical interventions when it occurs in the context of central nervous system psychopathology. Aggression can be categorized into various subtypes, including reactive/proactive, overt/covert, relational, and IA. IA in psychiatric or neurological disorders is reviewed along with current treatments, and an algorithm for systematic evaluation of aggression in the clinical setting is proposed. Conclusions: IA is a treatable form of maladaptive aggression that is distinct from other aggression subtypes. It occurs across diverse psychiatric and neurological diagnoses and affects a substantial subpopulation. IA can serve as an important construct in clinical practice and has considerable potential to advance research.

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Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder in children and adults. It is characterized by inappropriate levels of inattention (IA) and/or hyperactivity and impulsivity (HI). The ADHD diagnosis is hypothesized to represent the extreme of a continuous distribution of ADHD symptoms in the general population. In this study, we investigated whether factors linked to adult ADHD as a disorder are associated with adult ADHD symptoms in the general population. Our population-based sample included 4987 adults (mean age 56.1 years; 53.8% female) recruited by the Nijmegen Biomedical Study (NBS). Participants completed the Dutch ADHD DSM-IV Rating Scale for current and childhood ADHD symptoms, the Symptom Check List-90-R (SCL-90-R) anxiety subscale, and the Eysenk Personality Questionnaire (EPQR-S). Partial Spearman correlation and Hurdle negative binomial regression analysis were used to assess how age, sex, childhood ADHD symptoms, anxiety symptoms, and personality traits (neuroticism, extraversion, and psychoticism) are associated with current IA and HI symptoms. Increasing age was associated with a lower proportion of participants reporting HI symptoms and with reduced levels of HI; IA levels remained fairly stable over the age-range, but the probability of reporting IA symptoms increased throughout middle/late adulthood. Females were more likely to report IA symptoms than males. Childhood ADHD symptoms, neuroticism, and psychoticism were positively associated with current IA and HI symptoms, while extraversion had an opposite association with these symptom domains. Anxiety symptoms affected HI symptoms in females. Our results indicate that factors associated with categorical ADHD are also correlated with ADHD symptoms in the adult population.

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Pattern classification and stratification approaches have increasingly been used in research on Autism Spectrum Disorder (ASD) over the last ten years with the goal of translation towards clinical applicability. Here, we present an extensive scoping literature review on those two approaches. We screened a total of 635 studies, of which 57 pattern classification and 19 stratification studies were included. We observed large variance across pattern classification studies in terms of predictive performance from about 60% to 98% accuracy, which is among other factors likely linked to sampling bias, different validation procedures across studies, the heterogeneity of ASD and differences in data quality. Stratification studies were less prevalent with only two studies reporting replications and just a few showing external validation. While some identified strata based on cognition and intelligence reappear across studies, biology as a stratification marker is clearly underexplored. In summary, mapping biological differences at the level of the individual with ASD is a major challenge for the field now. Conceptualizing those mappings and individual trajectories that lead to the diagnosis of ASD, will become a major challenge in the near future.

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Normative models are a class of emerging statistical techniques useful for understanding the heterogeneous biology underlying psychiatric disorders at the level of the individual participant. Analogous to normative growth charts used in paediatric medicine for plotting child development in terms of height or weight as a function of age, normative models chart variation in clinical cohorts in terms of mappings between quantitative biological measures and clinically relevant variables. An emerging body of literature has demonstrated that such techniques are excellent tools for parsing the heterogeneity in clinical cohorts by providing statistical inferences at the level of the individual participant with respect to the normative range. Here, we provide a unifying review of the theory and application of normative modelling for understanding the biological and clinical heterogeneity underlying mental disorders. We first provide a statistically grounded yet non-technical overview of the conceptual underpinnings of normative modelling and propose a conceptual framework to link the many different methodological approaches that have been proposed for this purpose. We survey the literature employing these techniques, focusing principally on applications of normative modelling to quantitative neuroimaging-based biomarkers in psychiatry and, finally, we provide methodological considerations and recommendations to guide future applications of these techniques. We show that normative modelling provides a means by which the importance of modelling individual differences can be brought from theory to concrete data analysis procedures for understanding heterogeneous mental disorders and ultimately a promising route towards precision medicine in psychiatry.

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OBJECTIVE: The benefits of long-term use of methylphenidate treatment in children and adolescents with attention deficit hyperactivity disorder (ADHD), as frequently prescribed in clinical practice, are unclear. The authors investigated whether methylphenidate remains beneficial after 2 years of use. METHODS: Ninety-four children and adolescents (ages 8-18 years) who had been treated in regular care with methylphenidate for more than 2 years were randomly assigned to double-blind continuation of treatment for 7 weeks (36 or 54 mg/day of extended-release methylphenidate) or gradual withdrawal over 3 weeks, to 4 weeks of placebo. The primary outcome measure was the investigator-rated ADHD Rating Scale (ADHD-RS); secondary outcome measures were the investigator-rated Clinical Global Impressions improvement scale (CGI-I) and the Conners' Teacher Rating Scale-Revised: Short Form (CTRS-R:S). Continuous ratings were analyzed with mixed model for repeated measures analyses, and the CGI-I with a chi-square test. RESULTS: The mean ADHD-RS scores at baseline for the continuation and discontinuation groups, respectively, were 21.4 (SD=9.7) and 19.6 (SD=8.9); after 7 weeks, the mean scores were 21.9 (SD=10.8) and 24.7 (SD=11.4), with a significant between-group difference in change over time of -4.6 (95% CI=-8.7, -0.56) in favor of the group that continued methylphenidate treatment. The ADHD-RS inattention subscale and the CTRS-R:S ADHD index and hyperactivity subscale also deteriorated significantly more in the discontinuation group. The CGI-I indicated worsening in 40.4% of the discontinuation group, compared with 15.9% of the continuation group. CONCLUSIONS: Continued treatment with methylphenidate remains effective after long-term use. Some individual patients may, however, be withdrawn from methylphenidate without deterioration. This finding supports guideline recommendations that patients be assessed periodically to determine whether there is a continued need for methylphenidate treatment.

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The widely reported association between ADHD and overweight may be attributable to genetic and environmental factors also present in unaffected family members. Therefore, the purpose of this study was to examine the association between ADHD and overweight within families. A cohort was used of families with at least one member with ADHD, recruited as part of the Dutch node of the International Multicenter ADHD Genetics (IMAGE) study, with assessments taking place between 2003 and 2006, 2009 and 2012, and 2013 and 2015. The three assessment waves yielded N = 1828 youth assessments and N = 998 parent assessments from N = 447 unique families. Overweight was defined as a body mass index (BMI) ≥ 85th percentile for youth of the same age and sex; overweight in adults as a BMI ≥ 25. Effects of age, gender, and medication use (psychostimulants, antipsychotics, and melatonin) were taken into account. Generalized estimation equations were used to correct for within-family and within-subject correlations. There was no difference in risk between ADHD-affected youth and their unaffected siblings (OR 0.92, 95% CI 0.78-1.09). However, compared to population prevalence data, all ADHD family members alike were at increased risk for being overweight: ADHD-affected youth (OR 1.33, 95% CI 1.13-1.59), unaffected siblings (OR 1.73, 95% CI 1.45-2.08), mothers (OR 1.74, 95% CI 1.40-2.17) and fathers (OR 1.78, 95% CI 1.46-2.15). Parental overweight-but not parental ADHD-was predictive of offspring overweight (mothers OR 1.40; 95% CI 1.14-1.73, fathers OR 1.83; 95% CI 1.41-2.36). Being overweight runs in ADHD families, yet is not specifically linked to ADHD within families. Shared unhealthy lifestyle factors (including nutrition, sleep, exercise, stress) as well as genetic factors shared by family members likely explain the findings.

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Background: Autism spectrum disorder (ASD) is characterised by persisting difficulties in everyday functioning. Adaptive behaviour is heterogeneous across individuals with ASD, and it is not clear to what extent early development of adaptive behaviour relates to ASD outcome in toddlerhood. This study aims to identify subgroups of infants based on early development of adaptive skills and investigate their association with later ASD outcome. Methods: Adaptive behaviour was assessed on infants at high (n = 166) and low (n = 74) familial risk for ASD between 8 and 36 months using the Vineland Adaptive Behavior Scales (VABS-II). The four domains of VABS-II were modelled in parallel using growth mixture modelling to identify distinct classes of infants based on adaptive behaviour. Then, we associated class membership with clinical outcome and ASD symptoms at 36 months and longitudinal measures of cognitive development. Results: We observed three classes characterised by decreasing trajectories below age-appropriate norms (8.3%), stable trajectories around age-appropriate norms (73.8%), and increasing trajectories reaching average scores by age 2 (17.9%). Infants with declining adaptive behaviour had a higher risk (odds ratio (OR) = 4.40; confidence interval (CI) 1.90; 12.98) for ASD and higher parent-reported symptoms in the social, communication, and repetitive behaviour domains at 36 months. Furthermore, there was a discrepancy between adaptive and cognitive functioning as the class with improving adaptive skills showed stable cognitive development around average scores. Conclusions: Findings confirm the heterogeneity of trajectories of adaptive functioning in infancy, with a higher risk for ASD in toddlerhood linked to a plateau in the development of adaptive functioning after the first year of life.

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This longitudinal study focused on early behavioural problems and autistic traits. In a stratified, population-derived sample of 119 children, mothers reported through questionnaires on externalizing, internalizing, and social-communicative characteristics of their child in infancy (14 months) and toddlerhood (37 months), and on autistic traits at preschool age (4-5 years). Children with consistently normal behaviour from infancy to toddlerhood showed lower autistic traits at preschool age than children with deviant behaviour on one or both time points. High autistic traits at preschool age were predominantly preceded by problems in interaction, communication, language, play, and affect in infancy and/or toddlerhood, but also by inattention in toddlerhood. Adequate support and specific interventions in these domains are needed in an attempt to diminish further derailment of the child's behaviour and development, and to prevent the full manifestation of ASD or related disorders such as ADHD.

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Despite the high clinical burden, little is known about pathophysiology underlying autism spectrum disorder (ASD). Recent resting-state functional magnetic resonance imaging (rs-fMRI) studies have found atypical synchronization of brain activity in ASD. However, no consensus has been reached on the nature and clinical relevance of these alterations. Here, we addressed these questions in four large ASD cohorts. Using rs-fMRI, we identified functional connectivity alterations associated with ASD. We tested for associations of these imaging phenotypes with clinical and demographic factors such as age, sex, medication status, and clinical symptom severity. Our results showed reproducible patterns of ASD-associated functional hyper- and hypoconnectivity. Hypoconnectivity was primarily restricted to sensory-motor regions, whereas hyperconnectivity hubs were predominately located in prefrontal and parietal cortices. Shifts in cortico-cortical between-network connectivity from outside to within the identified regions were shown to be a key driver of these abnormalities. This reproducible pathophysiological phenotype was partially associated with core ASD symptoms related to communication and daily living skills and was not affected by age, sex, or medication status. Although the large effect sizes in standardized cohorts are encouraging with respect to potential application as a treatment and for patient stratification, the moderate link to clinical symptoms and the large overlap with healthy controls currently limit the usability of identified alterations as diagnostic or efficacy readout.

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BACKGROUND: The neuroanatomical basis of autism spectrum disorder (ASD) has remained elusive, mostly owing to high biological and clinical heterogeneity among diagnosed individuals. Despite considerable effort toward understanding ASD using neuroimaging biomarkers, heterogeneity remains a barrier, partly because studies mostly employ case-control approaches, which assume that the clinical group is homogeneous. METHODS: Here, we used an innovative normative modeling approach to parse biological heterogeneity in ASD. We aimed to dissect the neuroanatomy of ASD by mapping the deviations from a typical pattern of neuroanatomical development at the level of the individual and to show the necessity to look beyond the case-control paradigm to understand the neurobiology of ASD. We first estimated a vertexwise normative model of cortical thickness development using Gaussian process regression, then mapped the deviation of each participant from the typical pattern. For this, we employed a heterogeneous cross-sectional sample of 206 typically developing individuals (127 males) and 321 individuals with ASD (232 males) (6-31 years of age). RESULTS: We found few case-control differences, but the ASD cohort showed highly individualized patterns of deviations in cortical thickness that were widespread across the brain. These deviations correlated with severity of repetitive behaviors and social communicative symptoms, although only repetitive behaviors survived corrections for multiple testing. CONCLUSIONS: Our results 1) reinforce the notion that individuals with ASD show distinct, highly individualized trajectories of brain development and 2) show that by focusing on common effects (i.e., the "average ASD participant"), the case-control approach disguises considerable interindividual variation crucial for precision medicine.

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BACKGROUND: Resting-state functional magnetic resonance imaging-based studies on functional connectivity in autism spectrum disorder (ASD) have generated inconsistent results. Interpretation of findings is further hampered by small samples and a focus on a limited number of networks, with networks underlying sensory processing being largely underexamined. We aimed to comprehensively characterize ASD-related alterations within and between 20 well-characterized resting-state networks using baseline data from the EU-AIMS (European Autism Interventions-A Multicentre Study for Developing New Medications) Longitudinal European Autism Project. METHODS: Resting-state functional magnetic resonance imaging data was available for 265 individuals with ASD (7.5-30.3 years; 73.2% male) and 218 typically developing individuals (6.9-29.8 years; 64.2% male), all with IQ > 70. We compared functional connectivity within 20 networks-obtained using independent component analysis-between the ASD and typically developing groups, and related functional connectivity within these networks to continuous (overall) autism trait severity scores derived from the Social Responsiveness Scale Second Edition across all participants. Furthermore, we investigated case-control differences and autism trait-related alterations in between-network connectivity. RESULTS: Higher autism traits were associated with increased connectivity within salience, medial motor, and orbitofrontal networks. However, we did not replicate previously reported case-control differences within these networks. The between-network analysis did reveal case-control differences showing on average 1) decreased connectivity of the visual association network with somatosensory, medial, and lateral motor networks, and 2) increased connectivity of the cerebellum with these sensory and motor networks in ASD compared with typically developing subjects. CONCLUSIONS: We demonstrate ASD-related alterations in within- and between-network connectivity. The between-network alterations broadly affect connectivity between cerebellum, visual, and sensory-motor networks, potentially underlying impairments in multisensory and visual-motor integration frequently observed in ASD.

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To get additional insight into the phenotype of attentional problems, we examined to what extent genetic and environmental factors explain covariation between lack of dispositional mindfulness and attention-deficit/hyperactivity disorder (ADHD) traits in youth, and explored the incremental validity of these constructs in predicting life satisfaction. We used data from a UK population-representative sample of adolescent twins (N = 1092 pairs) on lack of dispositional mindfulness [Mindful Attention Awareness Scale (MAAS)], ADHD traits [Conners' Parent Rating Scale-Revised (CPRS-R): inattentive (INATT) and hyperactivity/impulsivity (HYP/IMP) symptom dimensions] and life satisfaction (Students' Life Satisfaction Scale). Twin model fitting analyses were conducted. Phenotypic correlations (rp) between MAAS and CPRS-R (INATT: rp = 0.18, HYP/IMP: rp = 0.13) were small, but significant and largely explained by shared genes for INATT (% rp INATT-MAAS due to genes: 93%, genetic correlation rA = 0.37) and HYP/IMP (% rp HYP/IMP-MAAS due to genes: 81%; genetic correlation rA = 0.21) with no significant contribution of environmental factors. MAAS, INATT and HYP/IMP significantly and independently predicted life satisfaction. Lack of dispositional mindfulness, assessed as self-reported perceived lapses of attention (MAAS), taps into an aspect of attentional functioning that is phenotypically and genetically distinct from parent-rated ADHD traits. The clinically relevant incremental validity of both scales implicates that MAAS could be used to explore the underlying mechanisms of an aspect of attentional functioning that uniquely affects life satisfaction and is not captured by DSM-based ADHD scales. Further future research could identify if lack of dispositional mindfulness and high ADHD traits can be targeted by different therapeutic approaches resulting in different effects on life satisfaction.

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BACKGROUND: The present paper presents a fundamentally novel approach to model individual differences of persons with the same biologically heterogeneous mental disorder. Unlike prevalent case-control analyses, that assume a clear distinction between patient and control groups and thereby introducing the concept of an 'average patient', we describe each patient's biology individually, gaining insights into the different facets that characterize persistent attention-deficit/hyperactivity disorder (ADHD). METHODS: Using a normative modeling approach, we mapped inter-individual differences in reference to normative structural brain changes across the lifespan to examine the degree to which case-control analyses disguise differences between individuals. RESULTS: At the level of the individual, deviations from the normative model were frequent in persistent ADHD. However, the overlap of more than 2% between participants with ADHD was only observed in few brain loci. On average, participants with ADHD showed significantly reduced gray matter in the cerebellum and hippocampus compared to healthy individuals. While the case-control differences were in line with the literature on ADHD, individuals with ADHD only marginally reflected these group differences. CONCLUSIONS: Case-control comparisons, disguise inter-individual differences in brain biology in individuals with persistent ADHD. The present results show that the 'average ADHD patient' has limited informative value, providing the first evidence for the necessity to explore different biological facets of ADHD at the level of the individual and practical means to achieve this end.

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Oppositional defiant disorder, conduct disorder (ODD/CD), and autism spectrum disorder (ASD) share poor empathic functioning and have been associated with impaired emotional processing. However, no previous studies directly compared similarities and differences in these processes for the two disorders. A two-choice emotional valence detection task requiring differentiation between positive, negative, and neutral IAPS pictures was administered to 52 adolescents (12-19 years) with ODD/CD, 52 with ASD and 24 typically developing individuals (TDI). Callous-unemotional (CU) traits were assessed by self- and parent reports using the Inventory of callous-unemotional traits. Main findings were that adolescents with ODD/CD or ASD both performed poorer than TDI in terms of accuracy, yet only the TDI-not both clinical groups-had relatively most difficulty in discriminating between positive versus neutral pictures compared to neutral-negative or positive-negative contrasts. Poorer performance was related to a higher level of CU traits. The results of the current study suggest youth with ODD/CD or ASD have a diminished ability to detect emotional valence which is not limited to facial expressions and is related to a higher level of CU traits. More specifically, youth with ODD/CD or ASD seem to have a reduced processing of positive stimuli and/or lack a 'positive perception bias' present in TDI that could either contribute to the symptoms and/or be a result of having the disorder and may contribute to the comorbidity of both disorders.

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Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/ hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients.

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When an individual engages in a task, the associated evoked activities build upon already ongoing activity, shaped by an underlying functional connectivity baseline (Fox et al., 2009; Smith et al., 2009; Tavor et al., 2016). Building on the idea that rest represents the brain's full functional repertoire, we here incorporate the idea that task-induced functional connectivity modulations ought to be task-specific with respect to their underlying resting state functional connectivity. Various metrics such as clustering coefficient or average path length have been proposed to index processing efficiency, typically from single fMRI session data. We introduce a framework incorporating task potency, which provides direct access to task-specificity by enabling direct comparison between task paradigms. In particular, to study functional connectivity modulations related to cognitive involvement in a task we define task potency as the amplitude of a connectivity modulation away from its baseline functional connectivity architecture as observed during a resting state acquisition. We demonstrate the use of our framework by comparing three tasks (visuo-spatial working memory, reward processing, and stop signal task) available within a large cohort. Using task potency, we demonstrate that cognitive operations are supported by a set of common within-network interactions, supplemented by connections between large-scale networks in order to solve a specific task.

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